Methodologies, tools and languages for building ontologies. Where is their meeting point?

In this paper we review and compare the main methodologies, tools and languages for building ontologies that have been reported in the literature, as well as the main relationships among them. Ontology technology is nowadays mature enough: many methodologies, tools and languages are already available. The future work in this field should be driven towards the creation of a common integrated workbench for ontology developers to facilitate ontology development, exchange, evaluation, evolution and management, to provide methodological support for these tasks, and translations to and from different ontology languages. This workbench should not be created from scratch, but instead integrating the technology components that are currently available

A data warehouse to support web site automation

Background: \ud Due to the constant demand for new information and timely updates of services and content in order to satisfy the user’s needs, web site automation has emerged as a solution to automate several personalization and management activities of a web site. One goal of automation is the reduction of the editor’s effort and consequently of the costs for the owner. The other goal is that the site can more timely adapt to the behavior of the user, improving the browsing experience and helping the user in achieving his/her own goals. \ud \ud Methods: \ud A database to store rich web data is an essential component for web site automation. In this paper, we propose a data warehouse that is developed to be a repository of information to support different web site automation and monitoring activities. We implemented our data warehouse and used it as a repository of information in three different case studies related to the areas of e-commerce, e-learning, and e-news. \ud \ud Result: \ud The case studies showed that our data warehouse is appropriate for web site automation in different contexts. \ud \ud Conclusion: \ud In all cases, the use of the data warehouse was quite simple and with a good response time, mainly because of the simplicity of its structure.FCT - Science and Technology Foundation (SFRH/BD/22516/2005)project Site-O-Matic (POSC/EIA/58367/2004)São Paulo Research Foundation (FAPESP) (grants 2011/19850-9, 2012/13830-9

Web mining for the integration of data mining with business intelligence in web-based decision support systems

Web mining can be defined as the use of data mining techniques to automatically discover and extract information from web documents and services. A decision support system is a computer-based information sy Analysis stem that supports business or organizational decision-making activities. Data mining and business intelligence techniques can be integrated in order to develop more advanced decision support systems. In this chapter, the authors propose to use web mining as a process to develop advanced decision support systems in order to support the management activities of a website. They describe the Web mining process as a sequence of steps for the development of advanced decision support systems. By following such a sequence, the authors can develop advanced decision support systems, which integrate data mining with business intelligence, for websites.Sao Paulo Research Foundation (FAPESP) (grants 2011/19850-9 and 2012/13830-9

Web Log Data Warehousing and Mining for Intelligent Web Caching

We introduce intelligent web caching algorithms that employ predictive models of web requests; the general idea is to extend the LRU policy of web and proxy servers by making it sensible to web access models extracted from web log data using data mining techniques. Two approaches have been studied in particular, frequent patterns and decision trees. The experimental results of the new algorithms show substantial improvement over existing LRU-based caching techniques, in terms of hit rate. We designed and developed a prototypical system, which supports data warehousing of web log data, extraction of data mining models and simulation of the web caching algorithms

Usability in digitalen Kooperationsnetzwerken. Nutzertests und Logfile-Analyse als kombinierte Methode

Usability is a key factor when developing new applications. The interaction between the users and the application should be efficient, effective and engaging. Furthermore, a good usability includes a high error tolerance and an good learnability. Different methods allow the measurement of usability throughout the development (process). All methods have in common that the different employed steps like planning, conducting and evaluating are rather time-consuming. When end-users are included as subjects, usability tests are employed. Due to the high time-effort, usually ten or less tests are conducted. The thesis tries to solve this point by trying to combine usability tests and logfile analysis. The empirical work is two-folded. First, usability tests within a learning management system (LMS) are logged in the background. These logfiles are assigned to severe usability problems. Second, the paths of the severe usability problems are combined with logfile data from a real-world LMS that runs the same application. The real-world logfiles contain a period of about 300 days with 133 active users. Prior to the combination, both data sets converted into a similar format. Being a new procedure, the definite similarity value had to be specified by descriptive statistics and visual inspections. The final combination makes it possible to determine the severity of usability problems on the basis of real-world usage data. The proposed method offers a more precise overview of the occurrence of the found usability problems, independent of the test situation. This thesis provides additional value to the fields of (Web) Data Mining, Usability and Human-Computer Interaction (HCI). It also offers additional knowledge to the field of software development, quantitative and quantitative research as well as computer-supported cooperative work (CSCW) and learning management systems (LMS)